{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x6aa80f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6a61f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 21)\n",
    "y = 3*x+1\n",
    "y2 = (-1/3)*x+1\n",
    "plt.plot(x,y)\n",
    "plt.plot(x,y2)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
